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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingwang Xu Wei Wang Kuanquan Wang Suyu Dong Pengzhong Sun Jinwei Sun Gongning Luo Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China

Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumor detection, yet they require significant expertise in interpreting 3D ABUS images, a task fraught with distinctive challenges. Although Vision Transformers (ViT) display remarkable potential for image processing, their low inductive bias and significant data requirements pose obstacles, particularly in the data-constrained medical field. To mitigate these issues, we introduce a Mask-Recover strategy for pretraining Transformer models on 3D ABUS images, enhancing model adaptability and reducing the data demands of the ViT model. Moreover, recognizing the risk that ViTs’ average pooling approach may unintentionally mask small but vital features, we propose Dual-CapsViT, an inventive model combining Transformers and Capsule Networks. This integration affords efficient token routing while preserving fine-grained details. To reconcile potential inconsistencies between capsules and tokens, we engineer a novel dual-channel routing algorithm, strengthening the decoder’s performance. We benchmarked our models against well-known standards such as ResNet and ViT for classifying breast tumors in ABUS images. Our models exhibited superior performance, as evidenced by improved accuracy, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC) metrics, thereby affirming Dual-CapsViT’s potential to enhance breast cancer diagnostics.

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Fine-grained Feature and Template Reconstruction for TIR Object Tracking

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Liao, Donghai Shu, Xiu Li, Zhihui Liu, Qiao Yuan, Di Chang, Xiaojun He, Zhenyu Xidian University Guangzhou Institute of Technology Guangzhou510555 China Guangzhou University School of Computer Science and Cyber Engineering Guangzhou510006 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Chongqing Normal University National Center for Applied Mathematics Chongqing401331 China University of Technology Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology Sydney2007 Australia Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China

Thermal infrared (TIR) object tracking is a significant subject within the field of computer vision. Currently, TIR object tracking faces challenges such as insufficient representation of object texture information and underutilization of temporal information, which severely affects the tracking accuracy of TIR tracking methods. To address these issues, we propose a TIR object tracking method (called: FFTR) based on fine-grained feature and template reconstruction. Specifically, aiming at the fine-grained information of the TIR object, we employ a frequency channel attention mechanism that transforms TIR images into the frequency domain using discrete cosine transform features. By capturing the fine-grained feature of TIR images from the frequency domain, we enhance the model’s ability to comprehend these images. To better leverage temporal information, we utilize a template region reconstruction method. This method reconstructs the template from the previous frame based on the search area of the current frame, which is then incorporated into the attention computation for the subsequent frame, thereby improving the tracking capability of TIR objects. Extensive quantitative and qualitative experiments show that our method achieves competitive tracking performance on the TIR benchmarks. © 1991-2012 IEEE.

关键词： Object tracking

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Sampled-Data H∞control for Nonlinear Dynamical Intelligent Fuzzy Systems with Time-Varying Delay 34

Sampled-Data H∞Control for Nonlinear Dynamical Intelligent ...

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34th Chinese control and Decision Conference, CCDC 2022

作者： Qu, Zifang Du, Zhenbin Zhang, Guangyong Shandong Technology and Business University School of Mathematics and Information Science Yantai264003 China Yantai University School of Computer and Control Engineering Yantai264005 China Wuxi University Faculty of Science Wuxi214105 China

ISBN: (纸本)9781665478960

The manuscript explores the issue of the sampled-data H∞ control for the nonlinear intelligent fuzzy system which has time-varying delay. A T-S fuzzy model is employed to portray the system and the fuzzy controller is a sampled-data signal implemented by a zero-order holder. According to the stability theory of Lyapunov-Krasovskii functional, a fuzzy sampled-data H∞ control algorithm is gained. Moreover, by adopting linear matrix inequalities (LMIs), some sufficient conditions for the controller are acquired, which ensure the H∞ performance under a prescribed level. Furthermore, an application example of a mechanical system displays the proposed design. © 2022 IEEE.

关键词： Linear matrix inequalities

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Internal Energy Recycling in Fapbi3/Mxene for Enhanced Photocatalytic H2 Evolution

SSRN

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SSRN 2024年

作者： Wang, Feng Guo, Ziwang Liu, Lulu Gong, Rui Jia, Guangri Xu, Yanchao Wei, Shuting Zhang, Dantong Ding, Baofu Cui, Xiaoqiang State Key Laboratory of Automotive Simulation and Control School of Materials Science and Engineering Key Laboratory of Automobile Materials of MOE Jilin University 2699 Qianjin Street Changchun130012 China Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology CAS Shenzhen518055 China Key Laboratory of Materials Design and Quantum Simulation School of Science Changchun University No.6543 Satellite Road Changchun130022 China Faculty of Materials Science and Energy Engineering Shenzhen University of Advanced Technology Guangdong Shenzhen518055 China

Carrier recombination is one of the main factors in lowering charge separation efficiency, and thus severely restricts photocatalytic performance. In this work, we demonstrate a composite system of FAPbI3/MXene/Pt, featuring an internal energy cycling from the non-radiative fluorescence resonance energy transfer (FRET) between perovskite and MXene. That is, the energy of the emitted fluorescent photons is reabsorbed by each other. Recycling process exploits the energy released by carrier recombination and increases efficiency of charge separation. As a result, the composite of FAPbI3/MXene/Pt possesses a rapid H2 evolution rate of 2394 µmol g-1 h-1 under 1.5 AM simulated sunlight, which is more than one order of magnitude higher than that of FAPbI3/Pt (64 µmol g-1 h-1). The proposed insight not only provides an efficient strategy to promote photocatalytic H2 evolution based on perovskite-based composite, but also opens up opportunities to design other novel heterogeneous catalytic materials and promote their application potential in various catalytic fields. © 2024, The Authors. All rights reserved.

关键词： Perovskite

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Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

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computers, Materials & Continua 2021年第11期69卷 2295-2312页

作者： Mohamed Esmail Karar Omar Reyad Mohammed Abd-Elnaby Abdel-Haleem Abdel-Aty Marwa Ahmed Shouman College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Mathematics and Computer Science Faculty of ScienceSohag UniversitySohag82524Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Physics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt

Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 ***,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 *** this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US *** to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware *** main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US *** image dataset(POCUS)was used to validate our proposed COVID-LWNet framework *** classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this *** results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,*** paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.

关键词： Coronavirus medical image processing artificial intelligence ultrasound

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Study on an Adaptive Clamping Device for Passive Safety Delivery of Vascular Intervention Robot

Study on an Adaptive Clamping Device for Passive Safety Deli...

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IEEE International Conference on Mechatronics and Automation

作者： Sheng Cao Shuxiang Guo Jian Guo Jian Wang Chunying Li Huiyin Xu Bin Wang Mingchao Ding Aerospace Center Hospital & the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology School of Life Science & Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China Shenzhen Institute of Advanced Biomedical Robot Company Limited Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China National Engineering Laboratory of Computer Technology for Big Data Systems Shenzhen University Shenzhen China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

In practical clinical applications, vascular intervention surgical robots have developed sophisticated human-machine interaction systems, enabling assistance to physicians in performing remote surgeries and providing intelligent visual feedback. However, regarding surgical safety, current research predominantly focuses on force feedback and robot control logic, with clamping mechanisms targeting the locking of catheters and guide wires. Excessive clamping force may lead to surface damage to intervention instruments, while insufficient force delivery may result from slippery surfaces. Therefore, addressing these issues, this study proposed a passive, compliant safety strategy for an adaptive clamping device based on a vascular intervention surgical robot platform. This device can accommodate different diameter catheters and maintain a constant delivery force during the intervention process. Finally, through experimentation, the effectiveness, safety, and stability of the device were demonstrated.

关键词： Visualization Medical robotics Force Wires Robot control Surgery Safety

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Multi-Band Low-Profile Non-Uniform Metasurface Antenna for Sub-6-GHz Applications

Multi-Band Low-Profile Non-Uniform Metasurface Antenna for S...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Naveed Hamid Nawaz Khan Botao Feng Li Deng Wei Yang Xiao Ding Qingsheng Zeng College of Electronics and Information Engineering Shenzhen University Shenzhen China College of Information and CommunicationEngineering Beijing University of Posts and Telecommunications Beijing China College of Big Data and Internet Shenzhen Technology University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China Department of Computer and Engineering University of Quebec in Outaouais Quebec Canada

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

This paper presents the design of a compact nonuniform metasurface antenna with dimensions of 70×70 mm 2 . The antenna basically comprises a 1.6 mm-thick metasurface patch and a 1 mm-thick substrate, separated by a 5-mm air gap to enhance performance. The proposed design is analyzed using CST Microwave Studio, evaluating key parameters such as Voltage Standing Wave Ratio (VSWR), |S11|, and radiation efficiency. Simulation results indicate excellent impedance matching, with |S11| below −10 dB across 2.35 GHz to 6 GHz, reaching a minimum of −36.647 dB at 5.16 GHz. The VSWR remains below 2.5, with a minimum of 1.5 at 5.16 GHz. Additionally, the antenna demonstrates high radiation efficiency, ranging 74% to 80.7%, indicating effective energy radiation. The experiment results highlight the potential of the proposed non-uniform metasurface antenna for next-generation wireless communication systems, especially for 5G and beyond, where compact size, broad bandwidth, and high efficiency are essential. Notably, the integration of a non-uniform metasurface geometry and strategically placed air gap enhances overall performance, making it a promising candidate for future wireless technologies.

关键词： Wireless communication 5G mobile communication Impedance matching Simulation Air gaps Voltage Metasurfaces Antennas Substrates Next generation networking

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Deep Robust Reversible Watermarking

arXiv

引用

arXiv 2025年

作者： Chen, Jiale Wang, Wei Shi, Chongyang Dong, Li Li, Yuanman Hu, Xiping School of Computer Science Beijing Institute of Technology Beijing100081 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China

Robust Reversible Watermarking (RRW) enables perfect recovery of cover images and watermarks in lossless channels while ensuring robust watermark extraction under lossy channels. However, existing RRW methods, mostly non-deep learning-based, suffer from complex designs, high computational costs, and poor robustness limiting their practical applications. To address these issues, this paper proposes Deep Robust Reversible Watermarking (DRRW), a deep learning-based RRW scheme. DRRW introduces an Integer Invertible Watermark Network (iIWN) to achieve an invertible mapping between integer data distributions, fundamentally addressing the limitations of conventional RRW approaches. Unlike traditional RRW methods requiring task-specific designs for different distortions, DRRW adopts an encoder-noise layer-decoder framework, enabling adaptive robustness against various distortions through end-to-end training. During inference, the cover image and watermark are mapped into an overflowed stego image and latent variables. Arithmetic coding efficiently compresses these into a compact bitstream, which is embedded via reversible data hiding to ensure lossless recovery of both the image and watermark. To reduce pixel overflow, we introduce an overflow penalty loss, significantly shortening the auxiliary bitstream while improving both robustness and stego image quality. Additionally, we propose an adaptive weight adjustment strategy that eliminates the need to manually preset the watermark loss weight, ensuring improved training stability and performance. Experiments on multiple datasets demonstrate that DRRW achieves notable performance advantages. Compared to state-of-the-art RRW methods, DRRW improves robustness and reduces embedding, extraction, and recovery complexities by 55.14×, 5.95×, and 3.57×, respectively. The auxiliary bitstream is shortened by 43.86×, and reversible embedding succeeds on 16,762 images in the PASCAL VOC 2012 dataset, marking a significant step toward pra

关键词： Image enhancement

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Multi-scale Context Intertwining for Panoramic Renal Pathology Segmentation

Multi-scale Context Intertwining for Panoramic Renal Patholo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ye Zhang Xianchao Guan Hengrui Li Xiangming Yan Ziyue Wang Yongbing Zhang School of Computer Science and Technology Harbin Institute of Technology Shenzhen Leibniz-Institut für Analytische Wissenschaften ISAS - e.V. Pengcheng Laboratory Shenzhen Faculty of Computing Harbin Institute of Technology Department of Electronic and Computer Engineering National University of Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Panoramic segmentation of renal pathological tissues plays a crucial role in diagnosing renal carcinoma and other kidney-related diseases. The multi-scale nature of kidney tissues, which requires different magnification levels for accurate analysis, presents a significant challenge for segmentation models. In this work, we propose a Multi-scale Context Intertwining Network (MCINet) to address this issue. Our approach utilizes an auxiliary interaction network to enhance feature interaction between different scales and generate pseudo-labels for unannotated structures. By incorporating exponential moving average strategies, we ensure seamless feature integration across scales. Extensive experiments demonstrate that MCINet outperforms state-of-the-art models in key metrics such as Dice and Hausdorff Distance, proving its efficacy in renal tissue segmentation tasks.

关键词： Measurement Image segmentation Pathology Analytical models Signal processing Acoustics Speech processing Optimization Kidney Diseases

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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IEEE International Conference on Big Data

作者： Krzysztof Hanzel Damian Grzechca Adam Ziebinski Lukas Chruszczyk Artur Janus Department of Telecommunications and Teleinformatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Distributed Systems and Informatics Devices Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Student of Electrical Engineering Faculty of Electrical Engineering Silesian University of Technology Gliwice Poland

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested; however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV – so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime.

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